The present invention relates to a vehicle control system, vehicle mounting apparatus, base station apparatus and vehicle control method, which can manage and control movement states of vehicles by collecting information about the vehicles or fixed vehicles through communication units and transmitting information processed or added at the base station further to the vehicles.
In a road traffic field, for the purpose of improving safety, efficiency, and environment; the road infrastructure has been upgraded and intelligence has been placed on cars to realize intelligent traffic. With regard to car automatic driving as its ultimate target, most conventional cars have been based on an autonomous system closed within the cars. Such intelligent car techniques include, for example, a technique by which magnetic nails buried in a road are detected by a magnetic sensor installed in a car to track the running lane following the magnetic nails and also to control the car in such a manner that the car is kept as spaced by a constant distance from cars running in front of and in back of the car under control of a laser radar or radio wave radar, and also include a technique by which lane markers are detected by a camera mounted on a car to realize its automatic driving, as disclosed in Technical Report of IEICE (The Institute of Electronics, Information and Communication Engineers of Japan), entitled "Placing Intelligence on Road Traffic", SANE96-49.
Meanwhile, car navigation systems have been rapidly spread these years and traffic congestion information has been broadcast from communication means such as FM multiplexing or various sorts of beacons. This is expected as a means for improving the efficiency of road traffic. Further, due to use of such an inexpensive sensor as a GPS or vibration gyro scope or due to a map matching technique of matching a current position with electronized map data, the prices of the car navigation systems have drop to such a level that the navigation systems can be widely used and spread, with a location accuracy as high (about 10 to 30 m) as substantially no erroneous road recognition.
However, the above auto-driving techniques involve enormous costs in the infrastructure upgrading by using magnetic nails and in maintenance thereof, which makes it highly difficult to maintain and improve nationwide roads. In addition, the in-car mounted camera method also causes other problems on the car side, that the car become costly, requires high processing performance and high reliability.
Further, the car navigation system has been successful in its costs with a positonal accuracy range of about 10 to 30 m as mentioned above, but it is practically impossible to realize the above car automatic driving with such levels of accracies. One of major causes of the low accuracies is due to the accuracy of a global positioning system (GPS). The GPS is said to have a positional error of 30 to 100 m in a point positioning manner currently used in the car navigation, this accuracy is increased to the above level by using another sensor or correcting or interpolating it through map matching. When a differential GPS (D-GPS) of transmitting GPS error correction information through communication means, which recently starts its service, is employed, the accuracy becomes about 1 to 10 m. Even this accuracy seems insufficient as an accuracy for the automatic driving. In the recent survey and position measuring fields, the GPS using the carrier phase differential GPS principle has been remarkably advanced and accuracies of 10 cm or less have been realized (real-time kinematic (RTK) position measurement system). These accuracies are satisfactory as automatic driving accuracies but its apparatus become expensive, thus hindering the apparatus from being practically implemented in cars. For details of these accurate GPSs, refer to a book entitled "basis of GPS measurement", written by Tsuchiya and Tuji, Nippon Sokuryou Kyoukai (1995).
Another of the causes of low accuracies other than sensor is that road map data is insufficient in accuracy. That is, most existing road data are network data based on node links, in which consideration is not paid to road width and detailed shape. There recently exists a detail map considering the above road width and detailed shape when it is limited in areas, but it has a problem that it involves enormous costs in data creation and maintenance.